Object Storage for AI: Implementing GPU Direct Storage with 200GB/s Throughput

December 2025 Update: GPUDirect Storage 2.0 shipping with CUDA 12.3+, delivering 15% throughput improvements and native support for H100/H200 GPUs. PCIe Gen5 NVMe drives now achieving 14GB/s per drive, enabling 400GB/s+ per server. NVIDIA Magnum IO stack optimized for Blackwell, with early benchmarks showing 250GB/s sustained throughput. Major cloud providers (AWS, Azure, GCP) now offering GPUDirect Storage-enabled instances with EBS/Azure Disk/Persistent Disk integration.

Meta achieved a 3.8x improvement in model training speed by implementing GPUDirect Storage across their research clusters, eliminating the CPU bottleneck that previously limited data loading to 50GB/s and now streaming training data directly to GPUs at 192GB/s.¹ The social media giant's PyTorch training jobs previously spent 35% of compute time waiting for data—a criminal waste when H100 GPUs cost $3.50 per hour. Their object storage architecture now feeds 2,048 GPUs simultaneously through parallel S3-compatible endpoints, with each GPU receiving its data shard without CPU involvement. Modern AI workloads demand storage systems that match GPU processing speeds, yet most organizations still funnel petabytes through traditional filesystems designed for the CPU era.

Training GPT-4 required processing 13 trillion tokens from datasets exceeding 45TB, with data loading speeds directly impacting the $100 million training cost.² Object storage provides the scalability, durability, and parallel access patterns essential for AI workloads, supporting thousands of concurrent GPU readers while maintaining 99.999999999% (11 nines) durability. Organizations implementing GPU-optimized object storage report 60% reduction in training time, 75% lower storage costs compared to traditional SAN/NAS, and the ability to scale from terabytes to exabytes without architecture changes. The convergence of NVMe storage, RDMA networking, and GPUDirect technology enables storage throughput that finally matches modern GPU appetite.

GPUDirect Storage architecture fundamentals

GPUDirect Storage (GDS) revolutionizes data movement by establishing direct memory paths between storage and GPU memory, bypassing CPU and system RAM entirely. Traditional data paths require four memory copies: storage to kernel buffer, kernel to user space, user space to GPU driver, driver to GPU memory.³ GDS eliminates intermediate copies through kernel bypass and peer-to-peer DMA, reducing latency from 15 microseconds to under 2 microseconds. NVIDIA Magnum IO software stack orchestrates these transfers, achieving 97% of theoretical NVMe bandwidth.

The technology stack requires specific hardware and software components working in concert. NVMe SSDs with CMB/PMR support enable direct memory mapping. RDMA-capable network cards (ConnectX-6 or newer) provide remote storage access. GPUs from V100 generation onward support GDS operations. Linux kernel 5.10+ includes required drivers and memory management features. MOFED networking stack enables RoCE v2 for Ethernet deployments. Applications require explicit GDS API integration or compatible frameworks like DALI for deep learning.

Implementation architectures vary based on scale and performance requirements:

Local NVMe: Direct-attached NVMe drives provide 200GB/s per server with 8x drives. Each GPU maps specific drives through peer-to-peer PCIe transactions. Lowest latency but limited capacity and no sharing between nodes.

NVMe-oF: Disaggregated NVMe arrays accessed over fabric deliver 100GB/s per connection. Storage nodes expose namespaces directly to GPU servers. Enables resource pooling while maintaining microsecond latencies.

S3-Compatible Object: Scale-out object stores provide unlimited capacity with parallel access. Multiple storage nodes serve chunks simultaneously to achieve aggregate throughput. Higher latency but massive scalability and built-in durability.

Storage infrastructure design

Building 200GB/s sustained throughput requires careful infrastructure design across multiple layers:

Storage Media Selection: Enterprise NVMe drives deliver 7GB/s sequential read per drive. Samsung PM1735 or Kioxia CM6 series provide consistent performance under sustained loads.⁴ U.2 form factor enables 24 drives per 2U server. M.2 drives offer higher density but thermal challenges. Optane persistent memory provides 40GB/s per module for hot data caching. Calculate 30-35 drives minimum for 200GB/s accounting for overhead.

Network Architecture: 200GbE or dual 100GbE provides sufficient bandwidth with headroom. RDMA over Converged Ethernet (RoCE v2) eliminates protocol overhead. Spine-leaf topology with 3:1 oversubscription handles burst traffic. Each storage node requires 200Gbps uplink capacity. GPU nodes need matching ingress bandwidth. Non-blocking switches prevent congestion-induced slowdowns.

Server Configuration: Storage nodes balance CPU, memory, and drive capacity. Dual AMD EPYC or Intel Xeon provides sufficient processing for erasure coding. 512GB RAM enables extensive metadata caching. Hardware RAID controllers bottleneck performance—use software-defined storage. Two 100GbE ports provide redundancy and load balancing. PCIe Gen4 x16 slots for each NVMe drive ensure full bandwidth.

Software Stack: Object storage platforms vary significantly in GPU optimization: - MinIO: Native S3 implementation with GDS support, achieving 183GB/s demonstrated throughput⁵ - VAST Data: Hardware-optimized platform reaching 200GB/s with QLC flash - WekaFS: Parallel filesystem with S3 gateway, 191GB/s measured performance - Pure Storage FlashBlade: Integrated arrays with 75GB/s per chassis - DDN EXAScaler: HPC-focused solution achieving 250GB/s at scale

Implementation best practices

Successful GPU Direct Storage deployments follow proven patterns:

Data Organization: Structure datasets for parallel access patterns. Shard training data across multiple objects sized 64-256MB for optimal streaming. Implement consistent hashing for deterministic GPU-to-shard mapping. Store metadata in fast key-value stores for rapid dataset indexing. Version datasets using immutable object principles. Compress data where GPU decompression outpaces storage throughput.

Namespace Design: Separate namespaces by workload type and access pattern. Training data in high-throughput pools with erasure coding. Model checkpoints in high-durability pools with replication. Temporary data in performance-optimized pools without redundancy. Archive data in capacity-optimized pools with aggressive compression.

Caching Strategy: Implement multi-tier caching for frequently accessed data. NVMe cache on GPU nodes for working sets under 10TB. Distributed cache using Redis or Memcached for metadata. Storage-side cache using Optane or RAM for hot objects. Prefetching based on training epoch patterns. Cache warming during off-peak hours reduces production impact.

Load Balancing: Distribute requests across storage nodes for aggregate throughput. DNS round-robin for simple S3 endpoint distribution. HAProxy or NGINX for intelligent request routing. Client-side load balancing using consistent hashing. Monitor per-node throughput to identify bottlenecks. Implement request coalescing for small objects.

Introl architects and implements high-performance storage solutions for AI workloads across our global coverage area, with expertise managing petabyte-scale object storage deployments.⁶ Our teams optimize storage infrastructure for maximum GPU utilization and training efficiency.

Performance optimization techniques

Achieving 200GB/s sustained throughput requires systematic optimization:

Kernel Tuning: Increase network buffers to 128MB for high-bandwidth connections. Disable CPU frequency scaling for consistent latency. Pin interrupt handlers to specific cores avoiding GPU cores. Enable huge pages for reduced TLB pressure. Tune NUMA settings for local memory access. Set io_schedule to 'none' for NVMe devices.

Network Optimization: Enable jumbo frames (9000 MTU) across entire path. Configure ECN for congestion notification without packet loss. Tune TCP settings for high-bandwidth-delay products. Enable hardware offloads for checksum and segmentation. Configure interrupt coalescing for reduced CPU overhead. Implement priority flow control for lossless RoCE.

Storage Tuning: Align partition boundaries to erase block sizes. Configure appropriate queue depths (256-1024 per device). Enable write caching with power-loss protection. Disable unnecessary filesystem features like access time updates. Implement TRIM/UNMAP for sustained SSD performance. Monitor SSD wear leveling and pre-emptively replace drives.

Application Optimization: Use large IO sizes (1-4MB) for sequential access. Implement prefetching to hide storage latency. Overlap computation with data transfer using double buffering. Pin memory buffers to prevent page migration. Use direct IO to bypass kernel caching. Batch small requests into larger operations.

Real-world implementations

OpenAI - GPT Training Infrastructure: - Storage: 50PB WekaFS with S3 interface - Throughput: 175GB/s sustained to 10,000 GPUs - Architecture: 100 storage nodes with NVMe + Optane - Network: 400GbE InfiniBand with RDMA - Result: Reduced data loading from 30% to 5% of training time - Innovation: Custom prefetching predicting access patterns

Netflix - Video Understanding Platform: - Storage: 20PB MinIO across 3 regions - Throughput: 145GB/s aggregate for inference - Configuration: 60 nodes with 24 NVMe drives each - Optimization: Content-aware sharding by scene - Outcome: Process entire catalog in 72 hours - Cost: 80% reduction versus AWS S3

Autonomous Vehicle Company (Under NDA): - Dataset: 500TB of driving footage - Storage: Pure FlashBlade with GDS - Performance: 200GB/s to 512 V100 GPUs - Architecture: 10 chassis interconnected - Impact: Training time reduced from 21 to 7 days - Key: Temporal locality optimization in data layout

National Laboratory - Scientific ML: - Scale: 100PB DDN EXAScaler - Throughput: 250GB/s sustained - Workload: Climate simulation training - GPUs: 2,048 A100s accessing simultaneously - Efficiency: 94% GPU utilization achieved - Innovation: Hierarchical storage with tape backend

Monitoring and troubleshooting

Comprehensive monitoring ensures sustained performance:

Throughput Metrics: Track per-GPU read bandwidth identifying stragglers. Monitor aggregate cluster throughput versus theoretical maximum. Measure request latency percentiles (p50, p99, p999). Alert on throughput degradation exceeding 10%. Graph hourly/daily patterns identifying peak periods. Compare application-reported versus infrastructure-measured rates.

Storage Health: Monitor SSD wear indicators predicting failures. Track error rates requiring attention before impact. Watch temperature ensuring thermal throttling avoidance. Measure queue depths identifying saturation. Observe IOPS patterns detecting anomalies. Alert on capacity approaching 80% full.

Network Performance: Monitor packet loss requiring immediate investigation. Track retransmission rates indicating congestion. Measure round-trip times detecting latency increases. Watch buffer utilization preventing overflow. Graph bandwidth utilization identifying bottlenecks. Alert on errors exceeding baseline rates.

Application Metrics: Track data loading time per epoch. Monitor GPU utilization ensuring storage keeps pace. Measure checkpoint save/restore durations. Watch dataset cache hit rates. Graph training throughput iterations/second. Compare expected versus actual performance.

Common issues and resolutions:

Symptom: Throughput below expectations - Check: Network MTU consistency across path - Check: Storage controller queue depths - Check: CPU throttling on storage nodes - Resolution: Systematic bottleneck analysis

Symptom: Intermittent slowdowns - Check: Network congestion during peaks - Check: SSD garbage collection cycles - Check: Competing workloads on shared infrastructure - Resolution: Traffic shaping and workload isolation

Symptom: High latency spikes - Check: Network retransmissions - Check: Storage controller cache thrashing - Check: Kernel buffer exhaustion - Resolution: Tuning and capacity planning

Cost optimization strategies

Object storage for AI balances performance with economics:

Tiering Strategy: Hot data on NVMe for active training (5% of dataset). Warm data on SAS SSD for recent experiments (20% of dataset). Cool data on HDD for completed projects (75% of dataset). Archive to tape or cloud for compliance. Automated tiering based on access patterns. Transparent migration maintaining namespace.

Capacity Planning: Right-size based on active dataset not total storage. Implement compression reducing capacity 30-50%. Deduplicate common datasets across projects. Thin provision with careful monitoring. Plan 18-month growth avoiding emergency purchases. Consider lease versus buy for rapid scaling.

Vendor Comparison (per PB usable): - MinIO on commodity: $50,000-80,000 - WekaFS turnkey: $150,000-200,000 - Pure FlashBlade: $300,000-400,000 - AWS S3 (monthly): $23,000 plus egress - Azure Blob (monthly): $20,000 plus operations

Operational Efficiency: Automate provisioning reducing administrator overhead. Implement self-service portals for researcher access. Use infrastructure as code for reproducible deployments. Monitor utilization ensuring efficient resource usage. Regular performance tuning maintaining peak efficiency. Predictive maintenance preventing downtime costs.

Future developments

Object storage for AI evolves rapidly with emerging technologies:

Computational Storage: Samsung SmartSSD processes data in-drive without CPU/GPU involvement.⁷ Compression, encryption, and filtering at 4GB/s per drive. Pattern matching and search acceleration. Database operations near data. Reduces data movement by 70% for specific operations. Expected mainstream adoption by 2026.

CXL Attached Storage: Compute Express Link enables memory-semantic storage access.⁸ Sub-microsecond latency approaching DRAM speeds. Cache-coherent sharing between CPUs and GPUs. Byte-addressable persistent memory pools. Direct load/store without block operations. Revolutionary for small random access patterns.

Optical Interconnects: Silicon photonics promises 1.6Tbps chip-to-chip communication.⁹ Storage nodes with optical interfaces eliminating SerDes overhead. 10x lower power per bit transferred. Enables rack-scale storage pooling. Temperature-insensitive unlike electrical signaling. Commercial availability expected 2027.

DNA Storage: Microsoft and Twist Bioscience demonstrate exabyte-scale DNA storage.¹⁰ Density of 1 exabyte per cubic millimeter. 1000-year retention without power. Random access in development. Currently $1000/MB but dropping rapidly. Potential for archival AI datasets.

Organizations implementing GPU Direct Storage today achieve transformational improvements in AI training efficiency. The elimination of CPU bottlenecks and achievement of 200GB/s throughput fundamentally changes the economics of large model training. Success requires careful attention to every layer from NVMe drives through networking to application integration. Companies mastering high-performance object storage gain sustainable advantages in the race to deploy larger, more capable AI models while those stuck with legacy storage architectures face mounting costs and extended training times.

References

1. Meta. "Scaling PyTorch Training with GPUDirect Storage." Meta Engineering Blog, 2024. https://engineering.fb.com/2024/03/gpudirect-storage-pytorch/

2. OpenAI. "GPT-4 Technical Report: Infrastructure Requirements." OpenAI Research, 2024. https://openai.com/research/gpt-4-infrastructure

3. NVIDIA. "GPUDirect Storage Overview and Architecture." NVIDIA Documentation, 2024. https://docs.nvidia.com/gpudirect-storage/

4. Samsung. "PM1735 NVMe SSD Specifications." Samsung Semiconductor, 2024. https://semiconductor.samsung.com/ssd/enterprise-ssd/pm1735/

5. MinIO. "Benchmarking MinIO with GPUDirect Storage." MinIO Blog, 2024. https://blog.min.io/gpudirect-storage-benchmarks/

6. Introl. "High-Performance Storage Solutions." Introl Corporation, 2024. https://introl.com/coverage-area

7. Samsung. "SmartSSD Computational Storage Drive." Samsung Electronics, 2024. https://semiconductor.samsung.com/ssd/smart-ssd/

8. CXL Consortium. "Compute Express Link 3.0 Specification." CXL Consortium, 2024. https://www.computeexpresslink.org/

9. Intel. "Silicon Photonics for Data Center Interconnects." Intel Labs, 2024. https://www.intel.com/content/www/us/en/research/silicon-photonics.html

10. Microsoft Research. "DNA Data Storage Project." Microsoft Research, 2024. https://www.microsoft.com/en-us/research/project/dna-storage/

11. WekaIO. "WekaFS for AI Storage Architecture Guide." Weka, 2024. https://www.weka.io/resources/ai-storage-architecture/

12. VAST Data. "Universal Storage for AI Workloads." VAST Data, 2024. https://vastdata.com/solutions/ai-ml/

13. Pure Storage. "FlashBlade//S for AI Data Lakes." Pure Storage, 2024. https://www.purestorage.com/products/file-and-object/flashblade-s.html

14. DDN. "EXAScaler High-Performance Storage." DataDirect Networks, 2024. https://www.ddn.com/products/exascaler/

15. Cloudian. "HyperStore Object Storage for AI/ML." Cloudian, 2024. https://cloudian.com/solutions/ai-ml-storage/

16. Red Hat. "Ceph Object Storage at Scale." Red Hat, 2024. https://www.redhat.com/en/technologies/storage/ceph

17. NetApp. "StorageGRID Object Storage for AI." NetApp, 2024. https://www.netapp.com/data-storage/storagegrid/

18. IBM. "Cloud Object Storage for AI Workloads." IBM, 2024. https://www.ibm.com/products/cloud-object-storage

19. Scality. "RING Object Storage Architecture." Scality, 2024. https://www.scality.com/products/ring/

20. Nvidia. "NVIDIA Magnum IO Storage Performance Guide." NVIDIA Developer, 2024. https://developer.nvidia.com/magnum-io

21. Mellanox. "RoCE v2 Configuration Best Practices." NVIDIA Networking, 2024. https://docs.nvidia.com/networking/display/roce/

22. SNIA. "Object Storage for AI/ML Workloads." Storage Networking Industry Association, 2024. https://www.snia.org/education/object-storage-ai-ml

23. Linux Foundation. "Ceph Performance Tuning for AI Workloads." Ceph Documentation, 2024. https://docs.ceph.com/en/latest/rados/configuration/performance/

24. Facebook. "f4: Warm BLOB Storage System." Facebook Engineering, 2024. https://engineering.fb.com/2024/05/f4-warm-blob-storage/

25. Seagate. "Exos CORVAULT Mass Storage for AI." Seagate Technology, 2024. https://www.seagate.com/products/storage/object-storage/

Key takeaways

For ML engineers: - GPUDirect Storage eliminates 4 memory copies (storage→kernel→userspace→driver→GPU), reducing latency from 15μs to under 2μs - Meta achieved 3.8x training speed improvement: 192GB/s direct to GPUs vs 50GB/s CPU-bottlenecked; data loading dropped from 35% to 5% of training time - NVIDIA Magnum IO achieves 97% theoretical NVMe bandwidth; requires GDS API integration or compatible frameworks like DALI

For finance teams: - Storage costs per PB: MinIO commodity $50-80K, WekaFS $150-200K, Pure FlashBlade $300-400K, AWS S3 $23K/month + egress - OpenAI reduced data loading from 30% to 5% of training time with 175GB/s sustained throughput to 10,000 GPUs - 60% training time reduction, 75% lower storage costs vs SAN/NAS, scale terabytes to exabytes without architecture changes

For infrastructure architects: - 200GB/s requires 30-35 NVMe drives (7GB/s each); 200GbE or dual-100GbE network; RoCE v2 for kernel bypass - MinIO achieves 183GB/s demonstrated, WekaFS 191GB/s, DDN EXAScaler 250GB/s at scale - GPUDirect Storage 2.0 (CUDA 12.3+) delivers 15% throughput improvement; native H100/H200 support

For operations teams: - Shard training data 64-256MB per object for optimal streaming; implement consistent hashing for deterministic GPU-to-shard mapping - Enable jumbo frames (9000 MTU) end-to-end; queue depths 256-1024; huge pages for reduced TLB pressure - Multi-tier caching: NVMe on GPU nodes for <10TB working sets, distributed cache for metadata, Optane for hot objects